{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook as tqdm\n",
    "import preprocessor as p\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %run ../twitter15/twitter15.ipynb\n",
    "%run ../twitter16/twitter16_text_processing.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    p.set_options(p.OPT.URL,p.OPT.MENTION,p.OPT.EMOJI,p.OPT.HASHTAG)\n",
    "    return p.tokenize(text).split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import json\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, f1_score, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import spacy\n",
    "from tqdm import tqdm, tqdm_notebook, tnrange\n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    p.set_options(p.OPT.URL, p.OPT.MENTION, p.OPT.EMOJI ,p.OPT.HASHTAG)\n",
    "    return p.tokenize(text).split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def indexer(split_text):\n",
    "    sent2idx = []\n",
    "    for w in split_text:\n",
    "        if w.lower() in word2idx:\n",
    "            sent2idx.append(word2idx[w.lower()])\n",
    "        else:\n",
    "            sent2idx.append(word2idx['_UNK'])\n",
    "            \n",
    "    return sent2idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 700/700 [00:00<00:00, 100280.51it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "'''\n",
    "STEP 1: LOADING DATASET\n",
    "'''\n",
    "\n",
    "labels = {'true':0,'false':1,'unverified':2,'non-rumor':3}\n",
    "\n",
    "data = list(twitter15_text.keys())\n",
    "random.shuffle(data)\n",
    "train_ids = data[:700]\n",
    "valid_ids = data[700:]\n",
    "train_text = [twitter15_text[x] for x in train_ids]\n",
    "valid_text = [twitter15_text[x] for x in valid_ids]\n",
    "\n",
    "train = pd.DataFrame({'text_id':train_ids ,'raw_text':train_text})\n",
    "train['clean_text'] = train.raw_text.apply(lambda x: preprocess(x.strip()))\n",
    "\n",
    "words = Counter()\n",
    "for sent in tqdm(train.clean_text.values):\n",
    "    words.update(w.lower() for w in sent)\n",
    "   \n",
    "# sort with most frequently occuring words first\n",
    "words = sorted(words, key=words.get, reverse=True)\n",
    "# add <pad> and <unk> token to vocab which will be used later\n",
    "words = ['_PAD','_UNK'] + words\n",
    "\n",
    "word2idx = {o:i for i,o in enumerate(words)}\n",
    "idx2word = {i:o for i,o in enumerate(words)}\n",
    "\n",
    "train['sentence2idx'] = train.clean_text.apply(lambda x: indexer(x))\n",
    "train['length'] = train.clean_text.apply(lambda x: len(x))\n",
    "train['label'] = train.text_id.apply(lambda x: labels[twitter15_labels[x]])\n",
    "\n",
    "valid = pd.DataFrame({'text_id':valid_ids ,'raw_text':valid_text})\n",
    "valid['clean_text'] = valid.raw_text.apply(lambda x: preprocess(x.strip()))\n",
    "\n",
    "valid['sentence2idx'] = valid.clean_text.apply(lambda x: indexer(x))\n",
    "valid['length'] = valid.clean_text.apply(lambda x: len(x))\n",
    "valid['label'] = valid.text_id.apply(lambda x: labels[twitter15_labels[x]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fulldata = pd.concat((train,valid))\n",
    "keys = list(fulldata['text_id'])\n",
    "vals = list(fulldata['sentence2idx'])\n",
    "sent2idx = dict(zip(keys, vals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VectorizeData(Dataset):\n",
    "    def __init__(self, df, maxlen=30):\n",
    "        self.maxlen = maxlen\n",
    "        self.df = df\n",
    "#         print('Padding')\n",
    "        self.df['padded_text'] = self.df.sentence2idx.apply(lambda x: self.pad_data(x))\n",
    "        \n",
    "    def __len__(self):\n",
    "        return self.df.shape[0]\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "#         lens = self.df.length[idx]\n",
    "        X = self.df.padded_text[idx]\n",
    "        y = self.df.label[idx]\n",
    "        lens = self.df.length[idx]\n",
    "        return X,y,lens\n",
    "    \n",
    "    def pad_data(self, s):\n",
    "        padded = np.zeros((self.maxlen,), dtype=np.int64)\n",
    "        if len(s) > self.maxlen: padded[:] = s[:self.maxlen]\n",
    "        else: padded[:len(s)] = s\n",
    "        return padded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = VectorizeData(train)\n",
    "valid_loader = VectorizeData(valid)\n",
    "tl = DataLoader(dataset=train_loader, batch_size=100, shuffle=True)\n",
    "vl = DataLoader(dataset=valid_loader, batch_size=100, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RecArch(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, num_layers, rnnType,device):\n",
    "        super(RecArch, self).__init__()\n",
    "        \n",
    "        self.vocab_size = vocab_size\n",
    "        self.embedding_dim = embedding_dim\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.num_layers = num_layers\n",
    "        self.device = device\n",
    "        self.rnnType = rnnType\n",
    "        \n",
    "        self.emb = nn.Embedding(self.vocab_size, embedding_dim)\n",
    "        \n",
    "        if self.rnnType == 'lstm':\n",
    "            self.recNN = nn.LSTM(embedding_dim,hidden_dim,num_layers,batch_first=True)\n",
    "            \n",
    "        if self.rnnType == 'gru':\n",
    "            self.recNN = nn.GRU(embedding_dim, hidden_dim, num_layers, batch_first=True)\n",
    "        \n",
    "        if self.rnnType == 'rnn':\n",
    "            self.recNN = nn.RNN(embedding_dim, hidden_dim, num_layers, batch_first=True, nonlinearity='tanh')\n",
    "        \n",
    "        self.fc = nn.Linear(hidden_dim,output_dim)\n",
    "    \n",
    "    def forward(self,x):\n",
    "        embs = self.emb(x)\n",
    "        embs = embs.view(x.size(0),-1,self.embedding_dim).to(self.device)\n",
    "        \n",
    "        h0 = Variable(torch.zeros(self.num_layers,x.size(0),self.hidden_dim),requires_grad=True).to(self.device)\n",
    "        \n",
    "        if self.rnnType == 'lstm':        \n",
    "            c0 = Variable(torch.zeros(self.num_layers,x.size(0),self.hidden_dim),requires_grad=True).to(self.device)\n",
    "            \n",
    "            out,(hn,cn) = self.recNN(embs,(h0,c0))\n",
    "        \n",
    "        else:\n",
    "            out, hn = self.recNN(embs, h0)\n",
    "        \n",
    "#         print(out[:,-1,:].shape)\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RecArch(\n",
       "  (emb): Embedding(3351, 256)\n",
       "  (recNN): GRU(256, 50, batch_first=True)\n",
       "  (fc): Linear(in_features=50, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_size = len(words)\n",
    "embedding_dim = 256\n",
    "n_hidden = 50\n",
    "n_out = 4\n",
    "num_layers = 1\n",
    "rnnType = 'gru'\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    device = 'cuda:2'\n",
    "else:\n",
    "    device = 'cpu'\n",
    "    \n",
    "model = RecArch(vocab_size,embedding_dim,n_hidden,n_out,num_layers,rnnType,device)\n",
    "model.to(device)\n",
    "model.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 8/200 [00:00<00:16, 11.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6779661016949152 recall 0.6903735632183907 prec: 0.7262227928588897 f1: 0.6622897900978219\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      1.00      0.68        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.83      0.75      0.79        32\n",
      "           3       0.83      0.34      0.49        29\n",
      "\n",
      "   micro avg       0.68      0.68      0.68       118\n",
      "   macro avg       0.73      0.69      0.66       118\n",
      "weighted avg       0.74      0.68      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  8%|▊         | 16/200 [00:01<00:14, 13.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6779661016949152 recall 0.6903735632183907 prec: 0.7262227928588897 f1: 0.6622897900978219\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      1.00      0.68        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.83      0.75      0.79        32\n",
      "           3       0.83      0.34      0.49        29\n",
      "\n",
      "   micro avg       0.68      0.68      0.68       118\n",
      "   macro avg       0.73      0.69      0.66       118\n",
      "weighted avg       0.74      0.68      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 12%|█▏        | 24/200 [00:01<00:13, 13.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6779661016949152 recall 0.6903735632183907 prec: 0.7204382183908046 f1: 0.6618163436966351\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.69      0.67      0.68        33\n",
      "           2       0.83      0.75      0.79        32\n",
      "           3       0.83      0.34      0.49        29\n",
      "\n",
      "   micro avg       0.68      0.68      0.68       118\n",
      "   macro avg       0.72      0.69      0.66       118\n",
      "weighted avg       0.73      0.68      0.66       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6779661016949152 recall 0.6903735632183907 prec: 0.7204382183908046 f1: 0.6618163436966351\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.69      0.67      0.68        33\n",
      "           2       0.83      0.75      0.79        32\n",
      "           3       0.83      0.34      0.49        29\n",
      "\n",
      "   micro avg       0.68      0.68      0.68       118\n",
      "   macro avg       0.72      0.69      0.66       118\n",
      "weighted avg       0.73      0.68      0.66       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6779661016949152 recall 0.6903735632183907 prec: 0.7165719696969698 f1: 0.6612009640750327\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.69      0.67      0.68        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.83      0.34      0.49        29\n",
      "\n",
      "   micro avg       0.68      0.68      0.68       118\n",
      "   macro avg       0.72      0.69      0.66       118\n",
      "weighted avg       0.72      0.68      0.66       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 44/200 [00:03<00:11, 13.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6779661016949152 recall 0.6903735632183907 prec: 0.7165719696969698 f1: 0.6612009640750327\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.69      0.67      0.68        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.83      0.34      0.49        29\n",
      "\n",
      "   micro avg       0.68      0.68      0.68       118\n",
      "   macro avg       0.72      0.69      0.66       118\n",
      "weighted avg       0.72      0.68      0.66       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 26%|██▌       | 52/200 [00:03<00:10, 13.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6779661016949152 recall 0.6903735632183907 prec: 0.7165719696969698 f1: 0.6612009640750327\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.69      0.67      0.68        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.83      0.34      0.49        29\n",
      "\n",
      "   micro avg       0.68      0.68      0.68       118\n",
      "   macro avg       0.72      0.69      0.66       118\n",
      "weighted avg       0.72      0.68      0.66       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 29%|██▉       | 58/200 [00:04<00:10, 13.47it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7253214527408076 f1: 0.6728463562844492\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.71      0.67      0.69        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.73      0.69      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7253214527408076 f1: 0.6728463562844492\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.71      0.67      0.69        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.73      0.69      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7253214527408076 f1: 0.6728463562844492\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.71      0.67      0.69        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.73      0.69      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 80/200 [00:05<00:08, 13.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7253214527408076 f1: 0.6728463562844492\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.71      0.67      0.69        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.73      0.69      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|████▍     | 88/200 [00:06<00:08, 13.55it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7253214527408076 f1: 0.6728463562844492\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.71      0.67      0.69        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.73      0.69      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|████▋     | 94/200 [00:06<00:07, 13.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7253214527408076 f1: 0.6728463562844492\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      1.00      0.71        24\n",
      "           1       0.71      0.67      0.69        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.73      0.69      0.67       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 51%|█████     | 102/200 [00:07<00:07, 13.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7282051282051282 f1: 0.6730169861305907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.74      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|█████▌    | 110/200 [00:08<00:06, 13.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7282051282051282 f1: 0.6730169861305907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.74      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 116/200 [00:08<00:06, 13.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7282051282051282 f1: 0.6730169861305907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.74      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 62%|██████▏   | 124/200 [00:09<00:05, 13.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7282051282051282 f1: 0.6730169861305907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.74      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7282051282051282 f1: 0.6730169861305907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.74      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7282051282051282 f1: 0.6730169861305907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.74      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|███████▏  | 144/200 [00:10<00:04, 13.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6864406779661016 recall 0.6989942528735632 prec: 0.7282051282051282 f1: 0.6730169861305907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.73      0.67      0.70        33\n",
      "           2       0.80      0.75      0.77        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.73      0.70      0.67       118\n",
      "weighted avg       0.74      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|███████▌  | 152/200 [00:11<00:03, 13.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 79%|███████▉  | 158/200 [00:11<00:03, 13.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 83%|████████▎ | 166/200 [00:12<00:02, 13.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 87%|████████▋ | 174/200 [00:12<00:01, 13.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 90%|█████████ | 180/200 [00:13<00:01, 13.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 188/200 [00:13<00:00, 13.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 194/200 [00:14<00:00, 13.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:14<00:00, 13.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 0.6949152542372882 recall 0.7068067528735632 prec: 0.7361398705113945 f1: 0.6806974776820499\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      1.00      0.70        24\n",
      "           1       0.76      0.67      0.71        33\n",
      "           2       0.81      0.78      0.79        32\n",
      "           3       0.85      0.38      0.52        29\n",
      "\n",
      "   micro avg       0.69      0.69      0.69       118\n",
      "   macro avg       0.74      0.71      0.68       118\n",
      "weighted avg       0.75      0.69      0.68       118\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adagrad(model.parameters(),lr=0.01)\n",
    "# criterion = torch.nn.BCEWithLogitsLoss()\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "count = 0\n",
    "seq_dim = 30\n",
    "num_epochs = 200\n",
    "\n",
    "train_losses_iterwise = []\n",
    "recall_iterwise = []\n",
    "precision_iterwise = []\n",
    "accuracy_iterwise = []\n",
    "f1score_iterwise = []\n",
    "val_losses_iterwise = []\n",
    "\n",
    "for epoch in tqdm(range(num_epochs)):\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    for i, (text,label,lengths) in enumerate(tl):\n",
    "\n",
    "        text = Variable(text.view(-1, seq_dim, 1)).to(device)\n",
    "        label = Variable(label).to(device)\n",
    "        \n",
    "#         print(sexism_label)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(text)\n",
    "        \n",
    "#         print(outputs)\n",
    "        \n",
    "        loss = criterion(outputs, label)\n",
    "        train_losses.append(loss.data.cpu())\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        count += 1\n",
    "        \n",
    "        if count % 50 == 0:    \n",
    "            correct = 0\n",
    "            total = 0\n",
    "\n",
    "            allLabels = []\n",
    "            allPreds = []\n",
    "            \n",
    "            for i, (text,label,lengths) in enumerate(vl):\n",
    "                labels=[]\n",
    "                text = Variable(text.view(-1, seq_dim, 1)).to(device)\n",
    "                label = Variable(label).to(device)\n",
    "                \n",
    "                predicted = model(text)\n",
    "                predicted =  torch.softmax(predicted,1)\n",
    "                predicted = torch.max(predicted, 1)[1].cpu().numpy().tolist()\n",
    "#                 print(predicted)\n",
    "#                 print(sexism_label)\n",
    "                allLabels += (label.cpu().numpy().tolist())\n",
    "                allPreds += (predicted)\n",
    "\n",
    "            valacc = accuracy_score(allLabels, allPreds)\n",
    "            recscore = recall_score(allLabels, allPreds,average='macro')\n",
    "            precscore = precision_score(allLabels, allPreds,average='macro')\n",
    "            f1score = f1_score(allLabels, allPreds,average='macro')\n",
    "            cr = classification_report(allLabels, allPreds)\n",
    "            print(f'acc: {valacc} recall {recscore} prec: {precscore} f1: {f1score}')\n",
    "            print(cr)\n",
    "            \n",
    "            train_losses_iterwise.append(np.mean(train_losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3351"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "textEncState = model.state_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Till this point the Text Model stats have been decided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter15_label_file = '../twitter16/label.txt'\n",
    "twitter15_text_file = '../twitter16/source_tweets.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_labels(file):\n",
    "    f = open(file,'r')\n",
    "    labels = {}\n",
    "    \n",
    "    raw_data = f.readlines()\n",
    "    \n",
    "    for line in raw_data:\n",
    "        line = line.strip()\n",
    "        line = line.split(':')\n",
    "        labels[int(line[1])] = line[0]\n",
    "    \n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{656955120626880512: 'false',\n",
       " 615689290706595840: 'true',\n",
       " 613404935003217920: 'false',\n",
       " 731166399389962242: 'unverified',\n",
       " 714598641827246081: 'unverified',\n",
       " 614467824313106432: 'true',\n",
       " 715515982584881152: 'unverified',\n",
       " 693315824132685824: 'non-rumor',\n",
       " 693843042546106369: 'non-rumor',\n",
       " 622891631293935616: 'false',\n",
       " 692630756548591616: 'non-rumor',\n",
       " 693265096278163456: 'non-rumor',\n",
       " 553589051044151296: 'true',\n",
       " 553590835850514433: 'true',\n",
       " 622858454949040128: 'false',\n",
       " 656870311057575936: 'false',\n",
       " 616311563071434753: 'true',\n",
       " 641666167992647681: 'non-rumor',\n",
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       " 714577521992343552: 'unverified',\n",
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       " 525028734991343617: 'true',\n",
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       " 667073349974102017: 'true',\n",
       " 524922729485848576: 'true',\n",
       " 668849913678209024: 'true',\n",
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       " 628336948810268673: 'true',\n",
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       " 552783667052167168: 'true',\n",
       " 552834961762709505: 'true',\n",
       " 716416753409081344: 'unverified',\n",
       " 707420972173955072: 'unverified',\n",
       " 780882510645370880: 'unverified',\n",
       " 553160652567498752: 'true',\n",
       " 544268732046913536: 'true',\n",
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       " 544282005941530624: 'true',\n",
       " 614609560658161664: 'true',\n",
       " 580348081100734464: 'true',\n",
       " 723504069814444033: 'unverified',\n",
       " 553586860334010368: 'true',\n",
       " 640118021101604864: 'unverified',\n",
       " 688751061503442944: 'non-rumor',\n",
       " 778530869208190976: 'unverified',\n",
       " 544291804057960448: 'true',\n",
       " 688004802245214208: 'non-rumor',\n",
       " 743126914794037248: 'unverified',\n",
       " 653432177632387072: 'unverified',\n",
       " 628604055644934144: 'true',\n",
       " 692163879909064704: 'non-rumor',\n",
       " 728273551883534336: 'unverified',\n",
       " 524942470472548352: 'true',\n",
       " 552792544132997121: 'true',\n",
       " 727834854931435522: 'unverified',\n",
       " 544289311504355328: 'true',\n",
       " 614494460747997184: 'true',\n",
       " 615592870540488704: 'true',\n",
       " 689661418484994049: 'non-rumor',\n",
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       " 714598318333042688: 'unverified',\n",
       " 656848877463560192: 'non-rumor',\n",
       " 692157602554343424: 'non-rumor',\n",
       " 765735409984806912: 'unverified',\n",
       " 614610102927147008: 'true',\n",
       " 614650850393391104: 'true',\n",
       " 614648099542204416: 'true',\n",
       " 544514564407427072: 'true',\n",
       " 707305999971921920: 'unverified',\n",
       " 644329878876237824: 'non-rumor',\n",
       " 553566026030272512: 'true',\n",
       " 715253497462145024: 'unverified',\n",
       " 693804277031157761: 'non-rumor',\n",
       " 715268937873760256: 'unverified',\n",
       " 763964234774347776: 'non-rumor',\n",
       " 614616994499788800: 'true',\n",
       " 693185853867294721: 'non-rumor',\n",
       " 553506608203169792: 'true',\n",
       " 751856580874960897: 'unverified',\n",
       " 712223438698627073: 'unverified',\n",
       " 707405809181917184: 'unverified',\n",
       " 693013768738115584: 'non-rumor',\n",
       " 744390771869102080: 'unverified',\n",
       " 524959809402331137: 'true',\n",
       " 778689027918618625: 'unverified',\n",
       " 707412869860696064: 'unverified',\n",
       " 650710483289419776: 'unverified',\n",
       " 656523458139045889: 'unverified',\n",
       " 693135382309896192: 'non-rumor',\n",
       " 623533663947517952: 'true',\n",
       " 692833140428017664: 'non-rumor',\n",
       " 614599815280857088: 'true',\n",
       " 693853310479155200: 'non-rumor',\n",
       " 614615157730357248: 'true',\n",
       " 714851393861881857: 'unverified',\n",
       " 767859423150759936: 'non-rumor',\n",
       " 764906140257837056: 'non-rumor',\n",
       " 619242759359037440: 'true',\n",
       " 552792913910833152: 'true',\n",
       " 764368020391223296: 'unverified',\n",
       " 741995157969592321: 'unverified',\n",
       " 767754921403899906: 'non-rumor',\n",
       " 692940257134653441: 'non-rumor',\n",
       " 667487418388488192: 'true',\n",
       " 553586897168392192: 'true',\n",
       " 730516765525082112: 'unverified',\n",
       " 693128224960856064: 'non-rumor',\n",
       " 553221600955621376: 'true',\n",
       " 693071538896162816: 'non-rumor',\n",
       " 693869818366287872: 'non-rumor',\n",
       " 714998347111669760: 'unverified',\n",
       " 686666933949837312: 'non-rumor',\n",
       " 767710042816602112: 'non-rumor',\n",
       " 714811995573325828: 'unverified',\n",
       " 728101712762834944: 'unverified',\n",
       " 544399927045283840: 'true',\n",
       " 524947674164760577: 'true',\n",
       " 701539698452393986: 'unverified',\n",
       " 653285570383335424: 'unverified',\n",
       " 552792802309181440: 'true',\n",
       " 707310135291416576: 'unverified',\n",
       " 693196937172951040: 'non-rumor',\n",
       " 614891047886434304: 'true',\n",
       " 525023025792835585: 'true',\n",
       " 765887221736046592: 'unverified',\n",
       " 767561970182598657: 'non-rumor',\n",
       " 687984820815851521: 'non-rumor',\n",
       " 544282227035869184: 'true',\n",
       " 766822417712963584: 'non-rumor',\n",
       " 553558982476828674: 'true',\n",
       " 544290258951892992: 'true',\n",
       " 650254360727973889: 'unverified',\n",
       " 544329935943237632: 'true',\n",
       " 693586539562037248: 'non-rumor',\n",
       " 764497123530375169: 'non-rumor',\n",
       " 724939017410727938: 'unverified',\n",
       " 651044059222556674: 'unverified',\n",
       " 552802654641225728: 'true',\n",
       " 723477822950395904: 'unverified',\n",
       " 544305540286148609: 'true',\n",
       " 693906624776245249: 'non-rumor',\n",
       " 524923462398513152: 'true',\n",
       " 763896522790440960: 'unverified',\n",
       " 616421546702336000: 'true',\n",
       " 524944399890124801: 'true',\n",
       " 686657138270277634: 'non-rumor',\n",
       " 544391176137089024: 'true',\n",
       " 524969201102901248: 'true',\n",
       " 756690088533393409: 'unverified',\n",
       " 767807335691653120: 'non-rumor',\n",
       " 552806757672964097: 'true',\n",
       " 693761289601060864: 'non-rumor',\n",
       " 544350567183556608: 'true',\n",
       " 758825535480864769: 'unverified',\n",
       " 707300612862566400: 'unverified',\n",
       " 693080409282846720: 'non-rumor',\n",
       " 614593386188828672: 'true',\n",
       " 692665281362202624: 'non-rumor',\n",
       " 767129248045948929: 'non-rumor',\n",
       " 692701223326056449: 'non-rumor',\n",
       " 732981604826677249: 'unverified',\n",
       " 692753210692476928: 'non-rumor',\n",
       " 650046859537448960: 'true',\n",
       " 693171092555431936: 'non-rumor',\n",
       " 693546915892428800: 'non-rumor',\n",
       " 544269749405097984: 'true',\n",
       " 760109079133990912: 'unverified',\n",
       " 779633844680962048: 'unverified',\n",
       " 765859710503378944: 'non-rumor'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter15_labels = load_labels(twitter15_label_file)\n",
    "twitter15_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    def __init__(self,uid,tid,time_stamp,label):\n",
    "        self.children = {}\n",
    "        self.childrenList = []\n",
    "        self.num_children = 0\n",
    "        self.tid = tid\n",
    "        self.uid = uid\n",
    "        self.label = label\n",
    "        self.time_stamp = time_stamp\n",
    "    \n",
    "    def add_child(self,node):\n",
    "        if node.uid not in self.children:\n",
    "            self.children[node.uid] = node\n",
    "            self.num_children += 1\n",
    "        else:\n",
    "            self.children[node.uid] = node\n",
    "        self.childrenList = list(self.children.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tree:\n",
    "    def __init__(self,root):\n",
    "        self.root = root\n",
    "        self.tweet_id = root.tid\n",
    "        self.uid = root.uid\n",
    "        self.height = 0\n",
    "        self.nodes = 0\n",
    "    \n",
    "    def show(self):\n",
    "        queue = [self.root,0]\n",
    "        \n",
    "        while len(queue) != 0:\n",
    "            toprint = queue.pop(0)\n",
    "            if toprint == 0:\n",
    "                print('\\n')\n",
    "            else:\n",
    "                print(toprint.uid,end=' ')\n",
    "                queue += toprint.children.values()\n",
    "                queue.append(0)\n",
    "                \n",
    "    def insertnode(self,curnode,parent,child):\n",
    "        if curnode.uid == parent.uid:\n",
    "            curnode.add_child(child)\n",
    "            return 1\n",
    "\n",
    "        elif parent.uid in curnode.children:\n",
    "            s = self.insertnode(curnode.children[parent.uid],parent,child)\n",
    "            return 2\n",
    "        else:\n",
    "            for node in curnode.children:\n",
    "                s = self.insertnode(curnode.children[node],parent,child)\n",
    "                if s == 2:\n",
    "                    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadPklFileNum(datapath,incSize,fileNum):\n",
    "    \n",
    "    with open(datapath+str(incSize)+'inc_'+str(fileNum)+'.pickle', 'rb') as handle:\n",
    "        twitTrees = pkl.load(handle)\n",
    "    return twitTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadTreeFilesOfIncrement(datapath,incSize):\n",
    "    twittertrees = {}\n",
    "    \n",
    "    files = [x for x in os.listdir(t15Datapath) if str(incSize)+'inc' in x]\n",
    "    \n",
    "    for file in tqdm(files):\n",
    "        with open(datapath+file,'rb') as handle:\n",
    "            partialTrees = pkl.load(handle)\n",
    "        twittertrees.update(partialTrees)\n",
    "        \n",
    "    return twittertrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "t15Datapath = '/home/nikhil.pinnaparaju/Research/Temporal Tree Encoding/twitter16/pickledTrees/'\n",
    "# twitter15_trees = loadPklFileNum(t15Datapath,20,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [01:22<00:00,  9.12s/it]\n"
     ]
    }
   ],
   "source": [
    "twitter15_trees = loadTreeFilesOfIncrement(t15Datapath,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = 'cuda:2'\n",
    "    device = 'cpu'\n",
    "else:\n",
    "    device = 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:00<00:00, 9479.60it/s]\n",
      "  0%|          | 0/33 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 33/33 [02:54<00:00,  5.29s/it]\n",
      "100%|██████████| 253378/253378 [02:11<00:00, 1927.06it/s]\n"
     ]
    }
   ],
   "source": [
    "%run ../twitter16/userdata_parser.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 253378/253378 [00:01<00:00, 196893.00it/s]\n"
     ]
    }
   ],
   "source": [
    "for key in tqdm(userVects):\n",
    "    userVects[key] = userVects[key].float()\n",
    "\n",
    "userVects = defaultdict(lambda:torch.tensor([1.1100e+02, 1.5000e+01, 0.0000e+00, 7.9700e+02, 4.7300e+02, 0.0000e+00,\n",
    "        8.3326e+04, 1.0000e+00]),userVects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ./temporal_tree_model.ipynb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'false': 0, 'true': 1, 'unverified': 2, 'non-rumor': 3}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelMap = {}\n",
    "labelCount = 0\n",
    "for label in list(twitter15_labels.values()):\n",
    "    if label not in labelMap:\n",
    "        labelMap[label] = labelCount\n",
    "        labelCount += 1\n",
    "labelMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "X = []\n",
    "y = []\n",
    "for tid in twitter15_trees:\n",
    "        if tid in twitter15_trees and tid in twitter15_labels:\n",
    "            X.append(twitter15_trees[tid])\n",
    "            y.append(twitter15_labels[tid])\n",
    "            \n",
    "x_train = X[:700]\n",
    "x_test = X[700:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "model = treeText(torch.cuda.is_available(),8,100,userVects,twitter15_labels,labelMap,criterion,device,vocab_size,textEncState)\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
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      "[0, 0, 0, 2, 1, 0, 1, 3, 2, 1, 0, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 0, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 1, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 2, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 1, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 2, 3, 2, 3, 0, 3, 1, 1, 1, 0, 1, 2, 0, 3] [0, 0, 0, 2, 1, 0, 1, 3, 3, 1, 3, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 2, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 2, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 3, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 0, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 3, 3, 2, 3, 0, 3, 1, 2, 1, 0, 1, 2, 0, 3]\n",
      "loss:  0.29023587703704834\n",
      "0.9322033898305084\n",
      "7\n"
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      "\n"
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      "\n",
      "[0, 0, 0, 2, 1, 0, 1, 3, 2, 1, 0, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 0, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 1, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 2, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 1, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 2, 3, 2, 3, 0, 3, 1, 1, 1, 0, 1, 2, 0, 3] [0, 0, 0, 2, 1, 0, 1, 3, 3, 1, 3, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 2, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 2, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 3, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 0, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 3, 3, 2, 3, 0, 3, 1, 2, 1, 0, 1, 2, 0, 3]\n",
      "loss:  0.2810232937335968\n",
      "0.9322033898305084\n",
      "8\n"
     ]
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      "\n",
      "[0, 0, 0, 2, 1, 0, 1, 3, 2, 1, 0, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 0, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 1, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 2, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 1, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 2, 3, 2, 3, 0, 3, 1, 1, 1, 0, 1, 2, 0, 3] [0, 0, 0, 2, 1, 0, 1, 3, 3, 1, 3, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 2, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 2, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 3, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 0, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 3, 3, 2, 3, 0, 3, 1, 2, 1, 0, 1, 2, 0, 3]\n",
      "loss:  0.2725045084953308\n",
      "0.9322033898305084\n",
      "9\n"
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      "\n"
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     "output_type": "stream",
     "text": [
      "\n",
      "[0, 0, 0, 2, 1, 0, 1, 3, 2, 1, 0, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 0, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 1, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 2, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 1, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 2, 3, 2, 3, 0, 3, 1, 1, 1, 0, 1, 2, 0, 3] [0, 0, 0, 2, 1, 0, 1, 3, 3, 1, 3, 0, 2, 1, 3, 2, 2, 0, 1, 0, 0, 3, 0, 0, 1, 2, 3, 2, 3, 3, 1, 1, 2, 2, 1, 0, 2, 3, 2, 2, 0, 0, 2, 2, 2, 0, 3, 0, 3, 1, 1, 1, 1, 0, 1, 3, 3, 0, 1, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 3, 2, 1, 3, 3, 3, 0, 3, 1, 1, 2, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 1, 2, 0, 2, 1, 2, 0, 2, 1, 3, 1, 0, 0, 3, 3, 2, 3, 0, 3, 1, 2, 1, 0, 1, 2, 0, 3]\n",
      "loss:  0.26760333776474\n",
      "0.9322033898305084\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adagrad(model.parameters(),0.01)\n",
    "\n",
    "maxAcc = 0\n",
    "count = 0\n",
    "netloss = 0\n",
    "\n",
    "for i in range(10):\n",
    "    print(i)\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    \n",
    "    for treeSet in tqdm_notebook(x_train):\n",
    "            tnum = 0\n",
    "            tree = treeSet[-1]\n",
    "#         for tree in treeSet:\n",
    "#             print(count)\n",
    "            count += 1\n",
    "#             tnum += 1\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            text = torch.tensor(sent2idx[tree.tweet_id])\n",
    "            text = Variable(text.view(-1, len(text), 1)).to(device)\n",
    "            \n",
    "            pred = model(tree.root,text)\n",
    "            \n",
    "            label = Variable(torch.tensor(labelMap[treeSet[0].root.label]).reshape(-1).to(device))\n",
    "            loss = criterion(pred.reshape(1,4),label)\n",
    "#             print(loss)\n",
    "            netloss += loss\n",
    "    \n",
    "            if count % 20 == 0:\n",
    "#                 print('opt')\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            \n",
    "    preds = []\n",
    "    labels = []\n",
    "\n",
    "    allLabels = []\n",
    "    allPreds = []\n",
    "\n",
    "    for valSet in tqdm_notebook(x_test):\n",
    "        finalTree = valSet[-1]\n",
    "        \n",
    "        text = torch.tensor(sent2idx[finalTree.tweet_id])\n",
    "        text = Variable(text.view(-1, len(text), 1)).to(device)\n",
    "        \n",
    "        predicted = model(finalTree.root,text)\n",
    "        preds.append(predicted)\n",
    "#         print(predicted)\n",
    "        predicted =  torch.softmax(predicted,0)\n",
    "        predicted = torch.max(predicted, 0)[1].cpu().numpy().tolist()\n",
    "\n",
    "        labels.append(labelMap[finalTree.root.label])\n",
    "\n",
    "        allLabels.append(labelMap[finalTree.root.label])\n",
    "        allPreds.append(predicted)\n",
    "\n",
    "    predTensor = torch.stack(preds)\n",
    "    labelTensor = torch.tensor(labels).to(device)\n",
    "\n",
    "    print(allLabels,allPreds)\n",
    "\n",
    "    loss = criterion(predTensor.reshape(-1,4), labelTensor.reshape(-1))\n",
    "\n",
    "    cr = classification_report(allLabels,allPreds,output_dict=True)\n",
    "    cr['loss'] = loss.item()\n",
    "    cr['Acc'] = accuracy_score(allLabels,allPreds,)\n",
    "    \n",
    "    if cr['Acc'] > maxAcc:\n",
    "        maxAcc = cr['Acc']\n",
    "        torch.save({'state_dict': model.state_dict()}, './earlydetect_twit16.pth')\n",
    "    \n",
    "    print('loss: ',cr['loss'])\n",
    "    print(cr['Acc'])\n",
    "    \n",
    "    with open('earlydetect_twit16.json', 'a') as fp:\n",
    "        json.dump(cr, fp)\n",
    "        fp.write('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Early Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint = torch.load('./earlydetect_twit16.pth')\n",
    "model.load_state_dict(checkpoint['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "import re\n",
    "import numpy as np\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# twitter15_trees = loadTreeFilesOfIncrement(t15Datapath,1)\n",
    "twitter15_trees = loadPklFileNum(t15Datapath,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "for tid in twitter15_trees:\n",
    "        if tid in twitter15_trees and tid in twitter15_labels:\n",
    "            x_test.append(twitter15_trees[tid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxTimeDiff(root):\n",
    "    current = root \n",
    "    \n",
    "#     print(current.childrenList)\n",
    "    while(current.childrenList):\n",
    "        current = current.childrenList[-1]\n",
    "    return current.time_stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "HBox(children=(IntProgress(value=0, max=98), HTML(value='')))"
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    },
    {
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    },
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "timeTaken = []\n",
    "\n",
    "for valSet in tqdm_notebook(x_test):\n",
    "    finalInd = len(valSet) - 1\n",
    "    \n",
    "    for i in tqdm(range(len(valSet))):\n",
    "        tree = valSet[i]\n",
    "        \n",
    "        text = torch.tensor(sent2idx[tree.tweet_id])\n",
    "        text = Variable(text.view(-1, len(text), 1)).to(device)\n",
    "        \n",
    "        predicted = model(tree.root,text)\n",
    "        predicted =  torch.softmax(predicted,0)\n",
    "        predicted = torch.max(predicted, 0)[1].cpu().numpy().tolist()\n",
    "        \n",
    "        if predicted == labelMap[tree.root.label]:\n",
    "            time = maxTimeDiff(tree.root)\n",
    "#             time = 0\n",
    "            timeTaken.append(time)\n",
    "            break\n",
    "        \n",
    "        else:\n",
    "            if i == finalInd:\n",
    "                time = maxTimeDiff(tree.root)\n",
    "                timeTaken.append(time)\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 0.0]"
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     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeTaken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.7985"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(np.array(timeTaken))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 7429.45,\n",
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     "execution_count": 50,
     "metadata": {},
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   "source": [
    "timeTaken"
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  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2568.8734693877554"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(np.array(timeTaken))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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